Adiposity, fitness, health-related quality of life and the reallocation of time between children's school day activity behaviours: A compositional data analysis.

Sedentary time (ST), light (LPA), and moderate-to-vigorous physical activity (MVPA) constitute the range of school day activity behaviours. This study investigated whether the composition of school activity behaviours was associated with health indicators, and the predicted changes in health when time was reallocated between activity behaviours. Accelerometers were worn for 7-days between October and December 2010 by 318 UK children aged 10-11, to provide estimates of school day ST, LPA, and MVPA. BMI z-scores and percent waist-to-height ratio were calculated as indicators of adiposity. Cardiorespiratory fitness (CRF) was assessed using the 20-m shuttle run test. The PedsQL™ questionnaire was completed to assess psychosocial and physical health-related quality of life (HRQL). Log-ratio multiple linear regression models predicted health indicators for the mean school day activity composition, and for new compositions where fixed durations of time were reallocated from one activity behaviour to another, while the remaining behaviours were unchanged. The school day activity composition significantly predicted adiposity and CRF (p = 0.04-0.002), but not HRQL. Replacing MVPA with ST or LPA around the mean activity composition predicted higher adiposity and lower CRF. When ST or LPA were substituted with MVPA, the relationships with adiposity and CRF were asymmetrical with favourable, but smaller predicted changes in adiposity and CRF than when MVPA was replaced. Predicted changes in HRQL were negligible. The school day activity composition significantly predicted adiposity and CRF but not HRQL. Reallocating time from ST and LPA to MVPA is advocated through comprehensive school physical activity promotion approaches. TRIAL REGISTRATION: ISRCTN03863885.

Individual calibration of accelerometers in children and their health-related implications.

This study compared children's physical activity (PA) levels, the prevalence of children meeting current guidelines of ≥60 minutes of daily moderate to vigorous PA (MVPA), and PA-health associations using individually calibrated (IC) and empirical accelerometer cutpoints. Data from 75 (n = 32 boys) 10-12 year old children were included in this study. Clustered cardiometabolic (CM) risk, directly measured cardiorespiratory fitness (CRF), anthropometric and 7 day accelerometer data were included within analysis. PA data were classified using Froude anchored IC, Evenson et al. (Evenson, K. R., Catellier, D. J., Gill, K., Ondrak, K. S., & McMurray, R. G. (2008). Calibration of two objective measures of physical activity for children. Journal of Sports Sciences, 26(14), 1557-1565. doi:10.1080/02640410802334196) (Ev) and Mackintosh et al. (Mackintosh, K. A., Fairclough, S. J., Stratton, G., & Ridgers, N. D. (2012). A calibration protocol for population-specific accelerometer cutpoints in children. PLoS One, 7(5), e36919. doi:10.1371/journal.pone.0036919) (Mack) cutpoints. The proportion of the cohort meeting ≥60mins MVPA/day ranged from 37%-56% depending on the cutpoints used. Reported PA differed significantly across the cutpoint sets. IC LPA and MPA were predictors of CRF (LPA: standardised ß = 0.32, p = 0.002, MPA: standardised ß = 0.27 p = 0.013). IC MPA also predicted BMI Z-score (standardised ß = -0.35, p = 0.004). Ev VPA was a predictor of BMI Z-score (standardised ß = -0.33, p = 0.012). Cutpoint choice has a substantial impact on reported PA levels though no significant associations with CM risk were observed. Froude IC cutpoints represent a promising approach towards classifying children's PA data.

The CHANGE! Project: Changes in Body Composition and Cardiorespiratory Fitness in 10- to 11-Year-Old Children After Completing the CHANGE! Intervention.

PURPOSE: To assess the effects of the Children's Health, Activity and Nutrition: Get Educated! intervention on body size, body composition, and peak oxygen uptake in a subsample of 10- to 11-year-old children. METHODS: Sixty children were recruited from 12 schools (N = 6 intervention) to take part in the CHANGE! subsample study. Baseline, postintervention, and follow-up measures were completed in October 2010, March-April 2011, and June-July 2011, respectively. Outcome measures were body mass index z score, waist circumference, body composition assessed using dual-energy X-ray absorptiometry (baseline and follow-up only), and peak oxygen uptake. RESULTS: Significant differences in mean trunk fat mass (control = 4.72 kg, intervention = 3.11 kg, P = .041) and trunk fat % (control = 23.08%, intervention = 17.75%, P = .022) between groups were observed at follow-up. Significant differences in waist circumference change scores from baseline to follow-up were observed between groups (control = 1.3 cm, intervention = -0.2 cm, P = .023). Favorable changes in body composition were observed in the intervention group; however, none of these changes reached statistical significance. No significant differences in peak oxygen uptake were observed. CONCLUSIONS: The results of the present study suggest the multicomponent curriculum intervention had small to medium beneficial effects on body size and composition health outcomes.

Utility of three anthropometric indices in assessing the cardiometabolic risk profile in children.

OBJECTIVES: To evaluate the ability of BMI, WC, and WHtR to identify increased cardiometabolic risk in pre-adolescents. METHODS: This is a cross-sectional study involving 192 children (10.92 ± 0.58 years, 56% female) from the United Kingdom between 2010 and 2013. Receiver operating characteristic curves determined the discriminatory ability of BMI, WC and WHtR to identify individuals with increased cardiometabolic risk (increased clustered triglycerides, HDL-cholesterol, systolic blood pressure, cardiorespiratory fitness, and glucose). RESULTS: A WHtR ≥ 0.5 increased the odds by 5.2 (95% confidence interval 2.6 - 10.3) of having increased cardiometabolic risk. Similar associations were observed for BMI and WC. Both BMI-z and WHtR were fair predictors of increased cardiometabolic risk, although BMI-z demonstrated the best trade-off between sensitivity and specificity, 76.1% and 63.6%, compared with 68.1% and 65.5% for WHtR. Cross-validation analysis revealed that BMI-z and WHtR correctly classified 84% of individuals (kappa score = 0.671, 95% CI 0.55, 0.79). The sensitivity of the cut-points suggests that 89.3% of individuals were correctly classified as being at risk with only 10.7% misdiagnosed whereas the specificity of the cut-points indicated that 77.8% of individuals were correctly identified as being healthy with 22.2% of individuals incorrectly diagnosed as being at risk. CONCLUSIONS: Findings suggest that WHtR provides similar cardiometabolic risk estimates to age and sex adjusted BMI.